A variety of data mining methods exist for automatically detecting relationships or associations between items stored or represented in a data repository. For example, in the context of an electronic catalog of items, data mining processes are commonly used to identify items that tend to be viewed, purchased, downloaded, or otherwise selected in combination. Different types of item relationships may be detected based on different types of user activity. For instance, a pair of items, A and B, may be identified as a likely complementary pair if a relatively large number of those who purchase A also purchase B.
A need exists, among other needs, in the data mining field to more effectively identify items that are useful in combination. Currently, some e-commerce web sites use purchase-based item relationships (“customers who bought A also bought B” or “customers who bought A also bought B and C”) to automatically select pairs of items to suggest purchasing in combination. Some bundles of items are also defined or selected manually by a human operator.